Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations1000
Missing cells91
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.1 KiB
Average record size in memory128.1 B

Variable types

Text1
Numeric9
Categorical4
Boolean2

Alerts

exam_score is highly overall correlated with study_hours_per_dayHigh correlation
study_hours_per_day is highly overall correlated with exam_scoreHigh correlation
parental_education_level has 91 (9.1%) missing values Missing
student_id has unique values Unique
study_hours_per_day has 13 (1.3%) zeros Zeros
social_media_hours has 21 (2.1%) zeros Zeros
netflix_hours has 59 (5.9%) zeros Zeros
exercise_frequency has 144 (14.4%) zeros Zeros

Reproduction

Analysis started2025-07-10 18:06:51.960244
Analysis finished2025-07-10 18:07:01.445297
Duration9.49 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

student_id
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:01.667054image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowS1000
2nd rowS1001
3rd rowS1002
4th rowS1003
5th rowS1004
ValueCountFrequency (%)
s1000 1
 
0.1%
s1013 1
 
0.1%
s1030 1
 
0.1%
s1029 1
 
0.1%
s1002 1
 
0.1%
s1003 1
 
0.1%
s1004 1
 
0.1%
s1005 1
 
0.1%
s1006 1
 
0.1%
s1007 1
 
0.1%
Other values (990) 990
99.0%
2025-07-10T20:07:02.007477image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1300
26.0%
S 1000
20.0%
0 300
 
6.0%
6 300
 
6.0%
7 300
 
6.0%
5 300
 
6.0%
8 300
 
6.0%
9 300
 
6.0%
2 300
 
6.0%
3 300
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
80.0%
Uppercase Letter 1000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1300
32.5%
0 300
 
7.5%
6 300
 
7.5%
7 300
 
7.5%
5 300
 
7.5%
8 300
 
7.5%
9 300
 
7.5%
2 300
 
7.5%
3 300
 
7.5%
4 300
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
S 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
80.0%
Latin 1000
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1300
32.5%
0 300
 
7.5%
6 300
 
7.5%
7 300
 
7.5%
5 300
 
7.5%
8 300
 
7.5%
9 300
 
7.5%
2 300
 
7.5%
3 300
 
7.5%
4 300
 
7.5%
Latin
ValueCountFrequency (%)
S 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1300
26.0%
S 1000
20.0%
0 300
 
6.0%
6 300
 
6.0%
7 300
 
6.0%
5 300
 
6.0%
8 300
 
6.0%
9 300
 
6.0%
2 300
 
6.0%
3 300
 
6.0%

age
Real number (ℝ)

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.498
Minimum17
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:02.132327image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile17
Q118.75
median20
Q323
95-th percentile24
Maximum24
Range7
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.3080995
Coefficient of variation (CV)0.11260121
Kurtosis-1.2189938
Mean20.498
Median Absolute Deviation (MAD)2
Skewness0.0084371397
Sum20498
Variance5.3273233
MonotonicityNot monotonic
2025-07-10T20:07:02.231236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20 146
14.6%
24 134
13.4%
17 133
13.3%
21 125
12.5%
23 119
11.9%
18 117
11.7%
19 113
11.3%
22 113
11.3%
ValueCountFrequency (%)
17 133
13.3%
18 117
11.7%
19 113
11.3%
20 146
14.6%
21 125
12.5%
22 113
11.3%
23 119
11.9%
24 134
13.4%
ValueCountFrequency (%)
24 134
13.4%
23 119
11.9%
22 113
11.3%
21 125
12.5%
20 146
14.6%
19 113
11.3%
18 117
11.7%
17 133
13.3%

gender
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Female
481 
Male
477 
Other
 
42

Length

Max length6
Median length5
Mean length5.004
Min length4

Characters and Unicode

Total characters5004
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 481
48.1%
Male 477
47.7%
Other 42
 
4.2%

Length

2025-07-10T20:07:02.347547image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T20:07:02.452317image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
female 481
48.1%
male 477
47.7%
other 42
 
4.2%

Most occurring characters

ValueCountFrequency (%)
e 1481
29.6%
a 958
19.1%
l 958
19.1%
F 481
 
9.6%
m 481
 
9.6%
M 477
 
9.5%
O 42
 
0.8%
t 42
 
0.8%
h 42
 
0.8%
r 42
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4004
80.0%
Uppercase Letter 1000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1481
37.0%
a 958
23.9%
l 958
23.9%
m 481
 
12.0%
t 42
 
1.0%
h 42
 
1.0%
r 42
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
F 481
48.1%
M 477
47.7%
O 42
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5004
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1481
29.6%
a 958
19.1%
l 958
19.1%
F 481
 
9.6%
m 481
 
9.6%
M 477
 
9.5%
O 42
 
0.8%
t 42
 
0.8%
h 42
 
0.8%
r 42
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1481
29.6%
a 958
19.1%
l 958
19.1%
F 481
 
9.6%
m 481
 
9.6%
M 477
 
9.5%
O 42
 
0.8%
t 42
 
0.8%
h 42
 
0.8%
r 42
 
0.8%

study_hours_per_day
Real number (ℝ)

High correlation  Zeros 

Distinct78
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5501
Minimum0
Maximum8.3
Zeros13
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:02.554730image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q12.6
median3.5
Q34.5
95-th percentile6
Maximum8.3
Range8.3
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.4688899
Coefficient of variation (CV)0.41376016
Kurtosis-0.055651868
Mean3.5501
Median Absolute Deviation (MAD)1
Skewness0.054253101
Sum3550.1
Variance2.1576376
MonotonicityNot monotonic
2025-07-10T20:07:02.675002image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 38
 
3.8%
3.2 36
 
3.6%
4.3 35
 
3.5%
3.3 34
 
3.4%
3.8 31
 
3.1%
4.1 26
 
2.6%
3.6 26
 
2.6%
3 25
 
2.5%
3.9 25
 
2.5%
2.5 24
 
2.4%
Other values (68) 700
70.0%
ValueCountFrequency (%)
0 13
1.3%
0.1 1
 
0.1%
0.2 1
 
0.1%
0.3 4
 
0.4%
0.5 4
 
0.4%
0.6 1
 
0.1%
0.7 5
 
0.5%
0.8 7
0.7%
0.9 3
 
0.3%
1 5
 
0.5%
ValueCountFrequency (%)
8.3 1
 
0.1%
8.2 1
 
0.1%
7.6 1
 
0.1%
7.5 1
 
0.1%
7.4 3
0.3%
7.3 1
 
0.1%
7.2 2
0.2%
7.1 1
 
0.1%
7 1
 
0.1%
6.9 1
 
0.1%

social_media_hours
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5055
Minimum0
Maximum7.2
Zeros21
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:02.935001image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.595
Q11.7
median2.5
Q33.3
95-th percentile4.5
Maximum7.2
Range7.2
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.1724224
Coefficient of variation (CV)0.4679395
Kurtosis-0.094082029
Mean2.5055
Median Absolute Deviation (MAD)0.8
Skewness0.1198052
Sum2505.5
Variance1.3745743
MonotonicityNot monotonic
2025-07-10T20:07:03.063383image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1 38
 
3.8%
2.9 36
 
3.6%
3.2 36
 
3.6%
2.2 35
 
3.5%
2.1 35
 
3.5%
3 34
 
3.4%
2.4 34
 
3.4%
1.9 32
 
3.2%
2.3 32
 
3.2%
2.8 31
 
3.1%
Other values (50) 657
65.7%
ValueCountFrequency (%)
0 21
2.1%
0.1 3
 
0.3%
0.2 8
 
0.8%
0.3 6
 
0.6%
0.4 6
 
0.6%
0.5 6
 
0.6%
0.6 8
 
0.8%
0.7 10
1.0%
0.8 10
1.0%
0.9 20
2.0%
ValueCountFrequency (%)
7.2 1
 
0.1%
6.2 1
 
0.1%
6.1 1
 
0.1%
6 1
 
0.1%
5.7 1
 
0.1%
5.6 1
 
0.1%
5.4 2
0.2%
5.3 1
 
0.1%
5.2 1
 
0.1%
5 4
0.4%

netflix_hours
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8197
Minimum0
Maximum5.4
Zeros59
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:03.197851image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1.8
Q32.525
95-th percentile3.6
Maximum5.4
Range5.4
Interquartile range (IQR)1.525

Descriptive statistics

Standard deviation1.0751176
Coefficient of variation (CV)0.59082133
Kurtosis-0.43285846
Mean1.8197
Median Absolute Deviation (MAD)0.8
Skewness0.2371544
Sum1819.7
Variance1.1558778
MonotonicityNot monotonic
2025-07-10T20:07:03.328009image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59
 
5.9%
2 48
 
4.8%
1.7 41
 
4.1%
1.4 40
 
4.0%
1.6 39
 
3.9%
2.3 37
 
3.7%
2.2 35
 
3.5%
2.4 34
 
3.4%
2.1 33
 
3.3%
0.9 33
 
3.3%
Other values (41) 601
60.1%
ValueCountFrequency (%)
0 59
5.9%
0.1 12
 
1.2%
0.2 11
 
1.1%
0.3 17
 
1.7%
0.4 17
 
1.7%
0.5 21
 
2.1%
0.6 19
 
1.9%
0.7 28
2.8%
0.8 21
 
2.1%
0.9 33
3.3%
ValueCountFrequency (%)
5.4 1
 
0.1%
5.3 1
 
0.1%
5 1
 
0.1%
4.9 1
 
0.1%
4.6 1
 
0.1%
4.5 1
 
0.1%
4.4 1
 
0.1%
4.3 5
0.5%
4.2 4
0.4%
4.1 7
0.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
785 
True
215 
ValueCountFrequency (%)
False 785
78.5%
True 215
 
21.5%
2025-07-10T20:07:03.421818image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

attendance_percentage
Real number (ℝ)

Distinct320
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.1317
Minimum56
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:03.520156image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile67.595
Q178
median84.4
Q391.025
95-th percentile100
Maximum100
Range44
Interquartile range (IQR)13.025

Descriptive statistics

Standard deviation9.3992463
Coefficient of variation (CV)0.11172063
Kurtosis-0.3907113
Mean84.1317
Median Absolute Deviation (MAD)6.5
Skewness-0.23781043
Sum84131.7
Variance88.345831
MonotonicityNot monotonic
2025-07-10T20:07:03.641638image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 66
 
6.6%
85.8 12
 
1.2%
85.3 8
 
0.8%
83.2 7
 
0.7%
81.7 7
 
0.7%
92.3 7
 
0.7%
79.9 7
 
0.7%
84.8 7
 
0.7%
85.2 7
 
0.7%
75.2 6
 
0.6%
Other values (310) 866
86.6%
ValueCountFrequency (%)
56 1
0.1%
56.7 1
0.1%
57.6 1
0.1%
59.5 1
0.1%
59.7 1
0.1%
59.8 1
0.1%
59.9 1
0.1%
60.6 1
0.1%
61 1
0.1%
61.2 1
0.1%
ValueCountFrequency (%)
100 66
6.6%
99.8 1
 
0.1%
99.5 2
 
0.2%
99.4 2
 
0.2%
99.1 1
 
0.1%
99 1
 
0.1%
98.9 2
 
0.2%
98.8 2
 
0.2%
98.6 4
 
0.4%
98.5 2
 
0.2%

sleep_hours
Real number (ℝ)

Distinct68
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4701
Minimum3.2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:03.758755image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4.595
Q15.6
median6.5
Q37.3
95-th percentile8.5
Maximum10
Range6.8
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.2263768
Coefficient of variation (CV)0.18954526
Kurtosis-0.21430896
Mean6.4701
Median Absolute Deviation (MAD)0.9
Skewness0.091483972
Sum6470.1
Variance1.504
MonotonicityNot monotonic
2025-07-10T20:07:03.885822image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 40
 
4.0%
6.1 36
 
3.6%
6.2 35
 
3.5%
6.7 34
 
3.4%
5.5 33
 
3.3%
7.1 33
 
3.3%
7 32
 
3.2%
6.6 32
 
3.2%
6.3 31
 
3.1%
5.4 30
 
3.0%
Other values (58) 664
66.4%
ValueCountFrequency (%)
3.2 1
 
0.1%
3.3 3
0.3%
3.4 1
 
0.1%
3.5 2
 
0.2%
3.6 3
0.3%
3.7 2
 
0.2%
3.8 3
0.3%
3.9 2
 
0.2%
4 5
0.5%
4.1 6
0.6%
ValueCountFrequency (%)
10 2
 
0.2%
9.8 1
 
0.1%
9.7 2
 
0.2%
9.6 1
 
0.1%
9.5 3
0.3%
9.4 2
 
0.2%
9.3 4
0.4%
9.2 1
 
0.1%
9.1 6
0.6%
9 4
0.4%

diet_quality
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Fair
437 
Good
378 
Poor
185 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFair
2nd rowGood
3rd rowPoor
4th rowPoor
5th rowFair

Common Values

ValueCountFrequency (%)
Fair 437
43.7%
Good 378
37.8%
Poor 185
18.5%

Length

2025-07-10T20:07:04.004969image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T20:07:04.087088image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
fair 437
43.7%
good 378
37.8%
poor 185
18.5%

Most occurring characters

ValueCountFrequency (%)
o 1126
28.1%
r 622
15.6%
F 437
 
10.9%
a 437
 
10.9%
i 437
 
10.9%
G 378
 
9.4%
d 378
 
9.4%
P 185
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3000
75.0%
Uppercase Letter 1000
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1126
37.5%
r 622
20.7%
a 437
 
14.6%
i 437
 
14.6%
d 378
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 437
43.7%
G 378
37.8%
P 185
18.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1126
28.1%
r 622
15.6%
F 437
 
10.9%
a 437
 
10.9%
i 437
 
10.9%
G 378
 
9.4%
d 378
 
9.4%
P 185
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1126
28.1%
r 622
15.6%
F 437
 
10.9%
a 437
 
10.9%
i 437
 
10.9%
G 378
 
9.4%
d 378
 
9.4%
P 185
 
4.6%

exercise_frequency
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.042
Minimum0
Maximum6
Zeros144
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:04.164152image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.025423
Coefficient of variation (CV)0.66581953
Kurtosis-1.2765261
Mean3.042
Median Absolute Deviation (MAD)2
Skewness-0.031922972
Sum3042
Variance4.1023383
MonotonicityNot monotonic
2025-07-10T20:07:04.254471image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 153
15.3%
6 152
15.2%
5 149
14.9%
1 146
14.6%
0 144
14.4%
4 134
13.4%
2 122
12.2%
ValueCountFrequency (%)
0 144
14.4%
1 146
14.6%
2 122
12.2%
3 153
15.3%
4 134
13.4%
5 149
14.9%
6 152
15.2%
ValueCountFrequency (%)
6 152
15.2%
5 149
14.9%
4 134
13.4%
3 153
15.3%
2 122
12.2%
1 146
14.6%
0 144
14.4%

parental_education_level
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing91
Missing (%)9.1%
Memory size7.9 KiB
High School
392 
Bachelor
350 
Master
167 

Length

Max length11
Median length8
Mean length8.9262926
Min length6

Characters and Unicode

Total characters8114
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaster
2nd rowHigh School
3rd rowHigh School
4th rowMaster
5th rowMaster

Common Values

ValueCountFrequency (%)
High School 392
39.2%
Bachelor 350
35.0%
Master 167
16.7%
(Missing) 91
 
9.1%

Length

2025-07-10T20:07:04.355485image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T20:07:04.437425image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
high 392
30.1%
school 392
30.1%
bachelor 350
26.9%
master 167
12.8%

Most occurring characters

ValueCountFrequency (%)
h 1134
14.0%
o 1134
14.0%
c 742
9.1%
l 742
9.1%
a 517
 
6.4%
e 517
 
6.4%
r 517
 
6.4%
H 392
 
4.8%
i 392
 
4.8%
g 392
 
4.8%
Other values (6) 1635
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6421
79.1%
Uppercase Letter 1301
 
16.0%
Space Separator 392
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 1134
17.7%
o 1134
17.7%
c 742
11.6%
l 742
11.6%
a 517
8.1%
e 517
8.1%
r 517
8.1%
i 392
 
6.1%
g 392
 
6.1%
s 167
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
H 392
30.1%
S 392
30.1%
B 350
26.9%
M 167
12.8%
Space Separator
ValueCountFrequency (%)
392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7722
95.2%
Common 392
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 1134
14.7%
o 1134
14.7%
c 742
9.6%
l 742
9.6%
a 517
6.7%
e 517
6.7%
r 517
6.7%
H 392
 
5.1%
i 392
 
5.1%
g 392
 
5.1%
Other values (5) 1243
16.1%
Common
ValueCountFrequency (%)
392
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 1134
14.0%
o 1134
14.0%
c 742
9.1%
l 742
9.1%
a 517
 
6.4%
e 517
 
6.4%
r 517
 
6.4%
H 392
 
4.8%
i 392
 
4.8%
g 392
 
4.8%
Other values (6) 1635
20.2%

internet_quality
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Good
447 
Average
391 
Poor
162 

Length

Max length7
Median length4
Mean length5.173
Min length4

Characters and Unicode

Total characters5173
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAverage
2nd rowAverage
3rd rowPoor
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Good 447
44.7%
Average 391
39.1%
Poor 162
 
16.2%

Length

2025-07-10T20:07:04.535965image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-10T20:07:04.624208image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
good 447
44.7%
average 391
39.1%
poor 162
 
16.2%

Most occurring characters

ValueCountFrequency (%)
o 1218
23.5%
e 782
15.1%
r 553
10.7%
G 447
 
8.6%
d 447
 
8.6%
A 391
 
7.6%
v 391
 
7.6%
a 391
 
7.6%
g 391
 
7.6%
P 162
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4173
80.7%
Uppercase Letter 1000
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1218
29.2%
e 782
18.7%
r 553
13.3%
d 447
 
10.7%
v 391
 
9.4%
a 391
 
9.4%
g 391
 
9.4%
Uppercase Letter
ValueCountFrequency (%)
G 447
44.7%
A 391
39.1%
P 162
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5173
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1218
23.5%
e 782
15.1%
r 553
10.7%
G 447
 
8.6%
d 447
 
8.6%
A 391
 
7.6%
v 391
 
7.6%
a 391
 
7.6%
g 391
 
7.6%
P 162
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1218
23.5%
e 782
15.1%
r 553
10.7%
G 447
 
8.6%
d 447
 
8.6%
A 391
 
7.6%
v 391
 
7.6%
a 391
 
7.6%
g 391
 
7.6%
P 162
 
3.1%

mental_health_rating
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.438
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:04.706882image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8475014
Coefficient of variation (CV)0.52363027
Kurtosis-1.1886019
Mean5.438
Median Absolute Deviation (MAD)2
Skewness0.0378107
Sum5438
Variance8.1082643
MonotonicityNot monotonic
2025-07-10T20:07:04.795015image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 110
11.0%
6 108
10.8%
8 105
10.5%
3 105
10.5%
1 102
10.2%
10 99
9.9%
5 99
9.9%
2 94
9.4%
7 91
9.1%
9 87
8.7%
ValueCountFrequency (%)
1 102
10.2%
2 94
9.4%
3 105
10.5%
4 110
11.0%
5 99
9.9%
6 108
10.8%
7 91
9.1%
8 105
10.5%
9 87
8.7%
10 99
9.9%
ValueCountFrequency (%)
10 99
9.9%
9 87
8.7%
8 105
10.5%
7 91
9.1%
6 108
10.8%
5 99
9.9%
4 110
11.0%
3 105
10.5%
2 94
9.4%
1 102
10.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
682 
True
318 
ValueCountFrequency (%)
False 682
68.2%
True 318
31.8%
2025-07-10T20:07:04.874901image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

exam_score
Real number (ℝ)

High correlation 

Distinct480
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.6015
Minimum18.4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-10T20:07:04.973920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum18.4
5-th percentile41.7
Q158.475
median70.5
Q381.325
95-th percentile99.305
Maximum100
Range81.6
Interquartile range (IQR)22.85

Descriptive statistics

Standard deviation16.888564
Coefficient of variation (CV)0.24264655
Kurtosis-0.41990756
Mean69.6015
Median Absolute Deviation (MAD)11.6
Skewness-0.15635066
Sum69601.5
Variance285.22359
MonotonicityNot monotonic
2025-07-10T20:07:05.109447image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 48
 
4.8%
80.9 7
 
0.7%
74 7
 
0.7%
70.7 7
 
0.7%
65.6 7
 
0.7%
71 6
 
0.6%
70.9 6
 
0.6%
76.1 6
 
0.6%
75.4 6
 
0.6%
66.7 5
 
0.5%
Other values (470) 895
89.5%
ValueCountFrequency (%)
18.4 1
0.1%
23.1 1
0.1%
26.2 1
0.1%
26.7 1
0.1%
26.8 2
0.2%
27.6 1
0.1%
28 1
0.1%
29.5 1
0.1%
29.7 1
0.1%
29.9 1
0.1%
ValueCountFrequency (%)
100 48
4.8%
99.9 1
 
0.1%
99.4 1
 
0.1%
99.3 1
 
0.1%
99 1
 
0.1%
98.8 2
 
0.2%
98.7 3
 
0.3%
98.5 1
 
0.1%
98.4 1
 
0.1%
98.3 1
 
0.1%

Interactions

2025-07-10T20:07:00.261442image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.171183image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.065363image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.927157image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.790963image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.627619image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.444695image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.482568image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.425981image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.369196image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.305982image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.183000image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.040381image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.889993image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.727567image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.537638image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.590005image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.518989image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.463329image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.397750image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.270500image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.130547image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.987229image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.814258image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.623909image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.689566image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.603744image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.562174image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.493317image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.361102image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.225307image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.078745image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.907851image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.725143image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.787974image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.696729image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.652687image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.592811image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.456923image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.314626image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.168903image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.997942image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.960143image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.879403image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.784145image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.739935image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.689466image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.548681image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.403694image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.260144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.079837image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.056974image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.991686image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.865765image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.834664image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.783653image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.638753image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.504726image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.353022image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.170046image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.156889image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.140880image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.958082image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.928755image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.875703image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.744195image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.596683image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.439751image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.261156image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.267769image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.241070image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.071121image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:01.018053image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:53.962873image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:54.831183image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:55.690193image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:56.524490image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:57.344948image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:58.380108image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:06:59.328159image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2025-07-10T20:07:00.157143image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2025-07-10T20:07:05.211488image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ageattendance_percentagediet_qualityexam_scoreexercise_frequencyextracurricular_participationgenderinternet_qualitymental_health_ratingnetflix_hoursparental_education_levelpart_time_jobsleep_hourssocial_media_hoursstudy_hours_per_day
age1.000-0.0230.000-0.010-0.0040.0790.0000.024-0.0460.0020.0000.0000.039-0.0120.002
attendance_percentage-0.0231.0000.0000.094-0.0090.0000.0200.052-0.008-0.0020.0320.0000.0120.0460.025
diet_quality0.0000.0001.0000.0550.0000.0510.0000.0000.0440.0000.0000.0000.0000.0000.047
exam_score-0.0100.0940.0551.0000.1500.0000.0000.0450.323-0.1650.0610.0000.123-0.1660.812
exercise_frequency-0.004-0.0090.0000.1501.0000.0500.0650.000-0.000-0.0080.0200.0490.019-0.034-0.038
extracurricular_participation0.0790.0000.0510.0000.0501.0000.0000.0000.0000.0000.0000.0000.0740.0870.000
gender0.0000.0200.0000.0000.0650.0001.0000.0350.0000.0000.0310.0000.0000.0000.037
internet_quality0.0240.0520.0000.0450.0000.0000.0351.0000.0500.0390.0000.0560.0000.0000.000
mental_health_rating-0.046-0.0080.0440.323-0.0000.0000.0000.0501.0000.0030.0430.066-0.005-0.004-0.009
netflix_hours0.002-0.0020.000-0.165-0.0080.0000.0000.0390.0031.0000.0000.000-0.0160.012-0.036
parental_education_level0.0000.0320.0000.0610.0200.0000.0310.0000.0430.0001.0000.0080.0000.0000.000
part_time_job0.0000.0000.0000.0000.0490.0000.0000.0560.0660.0000.0081.0000.0000.0000.051
sleep_hours0.0390.0120.0000.1230.0190.0740.0000.000-0.005-0.0160.0000.0001.0000.015-0.031
social_media_hours-0.0120.0460.000-0.166-0.0340.0870.0000.000-0.0040.0120.0000.0000.0151.0000.021
study_hours_per_day0.0020.0250.0470.812-0.0380.0000.0370.000-0.009-0.0360.0000.051-0.0310.0211.000

Missing values

2025-07-10T20:07:01.158979image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-10T20:07:01.351978image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

student_idagegenderstudy_hours_per_daysocial_media_hoursnetflix_hourspart_time_jobattendance_percentagesleep_hoursdiet_qualityexercise_frequencyparental_education_levelinternet_qualitymental_health_ratingextracurricular_participationexam_score
0S100023Female0.01.21.1No85.08.0Fair6MasterAverage8Yes56.2
1S100120Female6.92.82.3No97.34.6Good6High SchoolAverage8No100.0
2S100221Male1.43.11.3No94.88.0Poor1High SchoolPoor1No34.3
3S100323Female1.03.91.0No71.09.2Poor4MasterGood1Yes26.8
4S100419Female5.04.40.5No90.94.9Fair3MasterGood1No66.4
5S100524Male7.21.30.0No82.97.4Fair1MasterAverage4No100.0
6S100621Female5.61.51.4Yes85.86.5Good2MasterPoor4No89.8
7S100721Female4.31.02.0Yes77.74.6Fair0BachelorAverage8No72.6
8S100823Female4.42.21.7No100.07.1Good3BachelorGood1No78.9
9S100918Female4.83.11.3No95.47.5Good5BachelorGood10Yes100.0
student_idagegenderstudy_hours_per_daysocial_media_hoursnetflix_hourspart_time_jobattendance_percentagesleep_hoursdiet_qualityexercise_frequencyparental_education_levelinternet_qualitymental_health_ratingextracurricular_participationexam_score
990S199018Male3.23.51.7No91.76.5Good1MasterGood5No63.6
991S199120Male6.02.13.0No86.75.1Good2High SchoolGood3No85.3
992S199218Male3.50.01.9No96.86.4Fair3BachelorPoor3No71.8
993S199320Male3.82.11.0No89.05.2Good1High SchoolGood7No70.9
994S199420Female1.61.32.9No75.35.6Good0High SchoolAverage5No41.7
995S199521Female2.60.51.6No77.07.5Fair2High SchoolGood6Yes76.1
996S199617Female2.91.02.4Yes86.06.8Poor1High SchoolAverage6Yes65.9
997S199720Male3.02.61.3No61.96.5Good5BachelorGood9Yes64.4
998S199824Male5.44.11.1Yes100.07.6Fair0BachelorAverage1No69.7
999S199919Female4.32.91.9No89.47.1Good2BachelorAverage8No74.9